Ess-KE/East-Africa-Development-Dashboard-Economic-Social-Environmental-Trends-2000-2024-

An end-to-end data analytics project examining economic, social and environmental development trends across Ethiopia, Kenya, Rwanda, Tanzania and Uganda between 2000 and 2024. The project uses R for data cleaning, validation, exploratory analysis and statistical analysis, and Power BI for interactive visualization and dashboard development. # East Africa Development Dashboard — Economic, Social & Environmental Trends (2000–2024) ## Project Overview This project analyzes economic, social and environmental trends across five East African countries: - 🇪🇹 Ethiopia - 🇰🇪 Kenya - 🇷🇼 Rwanda - 🇹🇿 Tanzania - 🇺🇬 Uganda The analysis covers the period 2000–2024 and focuses on six World Development Indicators representing economic performance, social development, labour-market conditions and environmental impact. The project combines R, statistical analysis and Power BI to transform raw development data into an interactive dashboard and actionable insights. ## Objective The objective of this project was to examine how economic, social and environmental conditions changed across the selected East African countries between 2000 and 2024 and identify: - Long-term development trends - Differences in country-level performance - Changes between 2000 and 2024 - Significant variations and outliers - Relationships between key development indicators - Opportunities, risks and areas requiring policy attention ## Indicators Analyzed - GDP per Capita GDP per capita, constant 2015 US$ - GDP Growth Annual GDP growth (%) - Inflation Annual consumer price inflation (%) - Life Expectancy Life expectancy at birth (years) - Unemployment Total unemployment (% of labour force) - CO₂ Emissions CO₂ emissions excluding LULUCF per capita - Study period: 2000–2024 - Countries: 5 - Indicators: 6 - Observations: 750 ## Data Source - WDI Dataset The analysis was restricted to the five selected East African countries and six indicators relevant to the project's economic, social and environmental objectives. ## Data Cleaning & Preparation The data preparation process was performed in R and included: - Country and indicator filtering - Data type validation - Missing-value assessment - Duplicate checks - Year/date validation - Country and indicator label standardization - Data profiling - Distribution analysis - Quartile and IQR calcul …